Fast and accurate MRI reconstruction is a key concern in modern clinical practice.Recently,numerous Deep-Learning methods have been proposed for MRI reconstruction,however,they usually fail to reconstruct sharp detail...Fast and accurate MRI reconstruction is a key concern in modern clinical practice.Recently,numerous Deep-Learning methods have been proposed for MRI reconstruction,however,they usually fail to reconstruct sharp details from the subsampled k-space data.To solve this problem,we propose a lightweight and accurate Edge Attention MRI Reconstruction Network(EAMRI)to reconstruct images with edge guidance.Specifically,we design an efficient Edge Prediction Network to directly predict accurate edges from the blurred image.Meanwhile,we propose a novel Edge Attention Module(EAM)to guide the image reconstruction utilizing the extracted edge priors,as inspired by the popular self-attention mechanism.EAM first projects the input image and edges into Q_(image),K_(edge),and V_(image),respectively.Then EAM pairs the Q_(image)with K_(edge)along the channel dimension,such that 1)it can search globally for the high-frequency image features that are activated by the edge priors;2)the overall computation burdens are largely reduced compared with the traditional spatial-wise attention.With the help of EAM,the predicted edge priors can effectively guide the model to reconstruct high-quality MR images with accurate edges.Extensive experiments show that our proposed EAMRI outperforms other methods with fewer parameters and can recover more accurate edges.展开更多
Background:Ultra high field diffusion magnetic resonance imaging(dMRI)provides diffusion-weighted(DW)images with a high signal-to-noise ratio,but increases inhomogeneity,which affects the accuracy of dMRI metric recon...Background:Ultra high field diffusion magnetic resonance imaging(dMRI)provides diffusion-weighted(DW)images with a high signal-to-noise ratio,but increases inhomogeneity,which affects the accuracy of dMRI metric recon-struction.Current methods for correcting inhomogeneity rarely consider the accuracy of the reconstructed dMRI metrics.Deep learning models for reconstructing metrics from dMRI signals typically assume that DW images have a homogeneous intensity.To address these challenges,we propose a deep learning model capable of directly reconstructing high-accuracy dMRI metric maps from inhomogeneous DW images.Methods:An attention-based q-space inhomogeneity-resistant reconstruction network(qIRR-Net)is proposed for the voxel-wise reconstruction of diffusion tensor imaging and diffusion kurtosis imaging metrics.A training procedure based on data augmentation and consistency loss is introduced to ensure that the reconstruction results of qIRR-Net are not affected by signal in-homogeneity.The 3T and 7T dMRI data from the Human Connectome Project are used for model training,testing,and evaluation.Results:On the 3T dMRI data with simulated inhomogeneity,qIRR-Net improves the peak signal-to-noise ratio by 5.39 and the structural similarity index measure by 0.18 compared with weighted linear least-squares fitting.On the 7T dMRI data,the metric maps reconstructed by qIRR-Net not only exhibit clearer tissue structures but also demonstrate greater stability compared with the weighted linear least-squares results.Conclusions:The proposed qIRR-Net enables the accurate reconstruction of dMRI metrics from inhomogeneous DW images.This approach could poten-tially be expanded to obtain multiple artifact-free metric maps from ultrahigh field dMRI for neuroscience research and neurology applications.展开更多
Magnetic Resonance Imaging(MRI)serves as a crucial diagnostic tool in medical practice,yet its lengthy acquisition times pose challenges for patient comfort and clinical efficiency.Current deep learning approaches for...Magnetic Resonance Imaging(MRI)serves as a crucial diagnostic tool in medical practice,yet its lengthy acquisition times pose challenges for patient comfort and clinical efficiency.Current deep learning approaches for accelerating MRI acquisition face difficulties in managing data variability caused by different scanner vendors or imaging protocols.This research investigates the use of transfer learning in variational deep learning models to enhance generalization capabilities.We collect 135 ACR phantom samples from 3.0T GE and SIEMENS MRI scanners,following standard ACR guidelines,to study vendor-specific generalization.Additionally,the fastMRI brain dataset,a recognized benchmark for MRI acceleration,is utilized to evaluate performance across diverse acquisition sequences.Through comprehensive testing,we identify vendor and sequence inconsistencies as key hurdles for accelerated MRI generalization.To overcome these challenges,we introduce a feature refinement-based transfer learning method,achieving significant gains over baseline models in both vendor and sequence generalization tasks.Moreover,we incorporate experience replay to mitigate catastrophic forgetting,resulting in notable performance stability.For vendor generalization,our approach reduces Peak Signal Noise-to-Ratio(PSNR)and Structural SIMilarity(SSIM)degradation by 25.55%and 9.5%,respectively.Similarly,for sequence transfer,forgetting is reduced by 3.5%(PSNR)and 2%(SSIM),establishing a robust framework with substantial improvements.展开更多
基金This work is supported in part by the National Key R&D Program of China under Grant 2021YFE0203700 and 2021YFA1003004in part by the Natural Science Foundation of Shanghai under Grand 23ZR1422200+1 种基金in part by the Shanghai Sailing Program under Grant 23YF1412800in part by the NSFC/RGC N CUHK 415/19,Grant ITF MHP/038/20,Grant CRF 8730063,Grant RGC 14300219,14302920,14301121,and CUHK Direct Grant for Research.
文摘Fast and accurate MRI reconstruction is a key concern in modern clinical practice.Recently,numerous Deep-Learning methods have been proposed for MRI reconstruction,however,they usually fail to reconstruct sharp details from the subsampled k-space data.To solve this problem,we propose a lightweight and accurate Edge Attention MRI Reconstruction Network(EAMRI)to reconstruct images with edge guidance.Specifically,we design an efficient Edge Prediction Network to directly predict accurate edges from the blurred image.Meanwhile,we propose a novel Edge Attention Module(EAM)to guide the image reconstruction utilizing the extracted edge priors,as inspired by the popular self-attention mechanism.EAM first projects the input image and edges into Q_(image),K_(edge),and V_(image),respectively.Then EAM pairs the Q_(image)with K_(edge)along the channel dimension,such that 1)it can search globally for the high-frequency image features that are activated by the edge priors;2)the overall computation burdens are largely reduced compared with the traditional spatial-wise attention.With the help of EAM,the predicted edge priors can effectively guide the model to reconstruct high-quality MR images with accurate edges.Extensive experiments show that our proposed EAMRI outperforms other methods with fewer parameters and can recover more accurate edges.
基金National Natural Science Foundation of China,Grant/Award Numbers:61901465,82371910。
文摘Background:Ultra high field diffusion magnetic resonance imaging(dMRI)provides diffusion-weighted(DW)images with a high signal-to-noise ratio,but increases inhomogeneity,which affects the accuracy of dMRI metric recon-struction.Current methods for correcting inhomogeneity rarely consider the accuracy of the reconstructed dMRI metrics.Deep learning models for reconstructing metrics from dMRI signals typically assume that DW images have a homogeneous intensity.To address these challenges,we propose a deep learning model capable of directly reconstructing high-accuracy dMRI metric maps from inhomogeneous DW images.Methods:An attention-based q-space inhomogeneity-resistant reconstruction network(qIRR-Net)is proposed for the voxel-wise reconstruction of diffusion tensor imaging and diffusion kurtosis imaging metrics.A training procedure based on data augmentation and consistency loss is introduced to ensure that the reconstruction results of qIRR-Net are not affected by signal in-homogeneity.The 3T and 7T dMRI data from the Human Connectome Project are used for model training,testing,and evaluation.Results:On the 3T dMRI data with simulated inhomogeneity,qIRR-Net improves the peak signal-to-noise ratio by 5.39 and the structural similarity index measure by 0.18 compared with weighted linear least-squares fitting.On the 7T dMRI data,the metric maps reconstructed by qIRR-Net not only exhibit clearer tissue structures but also demonstrate greater stability compared with the weighted linear least-squares results.Conclusions:The proposed qIRR-Net enables the accurate reconstruction of dMRI metrics from inhomogeneous DW images.This approach could poten-tially be expanded to obtain multiple artifact-free metric maps from ultrahigh field dMRI for neuroscience research and neurology applications.
基金supported by the following grants:the National Institutes of Health(Nos.RF1AG073424 and P30AG072980)the Arizona Department of Health Services(No.CTR057001).
文摘Magnetic Resonance Imaging(MRI)serves as a crucial diagnostic tool in medical practice,yet its lengthy acquisition times pose challenges for patient comfort and clinical efficiency.Current deep learning approaches for accelerating MRI acquisition face difficulties in managing data variability caused by different scanner vendors or imaging protocols.This research investigates the use of transfer learning in variational deep learning models to enhance generalization capabilities.We collect 135 ACR phantom samples from 3.0T GE and SIEMENS MRI scanners,following standard ACR guidelines,to study vendor-specific generalization.Additionally,the fastMRI brain dataset,a recognized benchmark for MRI acceleration,is utilized to evaluate performance across diverse acquisition sequences.Through comprehensive testing,we identify vendor and sequence inconsistencies as key hurdles for accelerated MRI generalization.To overcome these challenges,we introduce a feature refinement-based transfer learning method,achieving significant gains over baseline models in both vendor and sequence generalization tasks.Moreover,we incorporate experience replay to mitigate catastrophic forgetting,resulting in notable performance stability.For vendor generalization,our approach reduces Peak Signal Noise-to-Ratio(PSNR)and Structural SIMilarity(SSIM)degradation by 25.55%and 9.5%,respectively.Similarly,for sequence transfer,forgetting is reduced by 3.5%(PSNR)and 2%(SSIM),establishing a robust framework with substantial improvements.
